Road anomaly early warning method, device, medium and electronic equipment

Abstract

The application discloses a road anomaly early warning method and device, a medium and an electronic equipment, and relates to the technical field of Internet of Vehicles. In the method, a vehicle-mounted device can detect the abnormal type, the abnormal influence range and the abnormal persistence information of a road anomaly on a road on which a vehicle travels; an abnormal severity index is obtained by calculating according to the abnormal type, the abnormal influence range and the abnormal persistence information; in response to the detection of the road anomaly, the abnormal early warning information corresponding to the road anomaly is generated, and a roadside device is detected; whether the abnormal early warning information is reported to the detected roadside device is determined according to the abnormal severity index; if yes, the abnormal early warning information is reported to the roadside device. The application can effectively improve the road warning reliability in the vehicle-road cooperation road warning scene, and effectively reduce the vehicle-road cooperation work load of the vehicle-mounted device and the roadside device.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, specifically to a road anomaly early warning method, device, medium, and electronic equipment. Background Technology

[0002] Road anomalies such as potholes, obstacles, and accidents may occur due to various reasons. In the current scenario of vehicle-to-everything (V2X) perception data sharing, there are solutions where vehicles use onboard equipment and roadside equipment to conduct road anomaly warnings through road collaboration.

[0003] In the current solution, the relevant technologies typically involve sharing road anomaly information directly with roadside equipment for early warning after the vehicle detects a road anomaly. However, this direct sharing can lead to various invalid warnings when the severity of the road anomaly is low, resulting in unreliable anomaly warnings. Furthermore, given the large amount of information involved in road coordination, directly sharing road anomaly information can unnecessarily increase the workload of both onboard and roadside equipment.

[0004] Therefore, the current road early warning system suffers from low reliability of vehicle-road cooperative road early warning and high workload of on-board and roadside equipment. Summary of the Invention

[0005] This application provides a solution that can effectively improve the reliability of road warnings in vehicle-road cooperative road warning scenarios and effectively reduce the vehicle-road cooperative workload of on-board equipment and roadside equipment.

[0006] The embodiments of this application provide the following technical solutions:

[0007] According to one embodiment of this application, a road anomaly early warning method is applied to an in-vehicle device. The method includes: detecting the anomaly type, anomaly impact range, and anomaly persistence information of a road anomaly on a road where the vehicle is traveling; calculating an anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information; generating an anomaly early warning information corresponding to the road anomaly in response to the detection of the road anomaly, and detecting roadside equipment; determining whether to report the anomaly early warning information to the detected roadside equipment based on the anomaly severity index; and if so, reporting the anomaly early warning information to the roadside equipment.

[0008] In some embodiments of this application, the step of determining whether to report the abnormality warning information to the detected roadside equipment based on the abnormality severity index includes: continuously detecting the road abnormality and determining the detection accuracy of the road abnormality during the vehicle's operation; and determining whether to report the abnormality warning information to the roadside equipment based on the abnormality severity index and the detection accuracy.

[0009] In some embodiments of this application, the step of calculating the anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information includes: determining an adjustment coefficient based on the current time period, road segment, and weather of the vehicle; mapping the anomaly type, the anomaly impact range, and the anomaly persistence information to second anomaly scores respectively; and calculating a weighted sum of the second anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information based on the adjustment coefficient to obtain the anomaly severity index.

[0010] In some embodiments of this application, generating abnormal warning information corresponding to the road abnormality includes: obtaining abnormal description information of the road abnormality, the abnormal description information including basic abnormal information and additional abnormal information, the basic abnormal information including at least the abnormal location and abnormal type, the additional abnormal information including at least one of abnormal severity index and detection accuracy; detecting the abnormal avoidance processing information of the vehicle in response to the road abnormality; and generating abnormal warning information including the abnormal description information and the abnormal avoidance processing information.

[0011] According to one embodiment of this application, a road anomaly early warning device is applied to vehicle-mounted equipment. The device includes: a detection module for detecting the anomaly type, anomaly impact range, and anomaly persistence information of a road anomaly on the road where the vehicle is traveling; a calculation module for calculating an anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information; a preparation module for generating an anomaly early warning information corresponding to the road anomaly in response to the detection of the road anomaly, and detecting roadside equipment; a determination module for determining whether to report the anomaly early warning information to the detected roadside equipment based on the anomaly severity index; and a reporting module for reporting the anomaly early warning information to the roadside equipment if the anomaly is detected.

[0012] In some embodiments of this application, the determining module is configured to: continuously detect road anomalies and determine the detection accuracy of the road anomalies during the vehicle's operation; and determine whether to report the anomaly warning information to the detected roadside equipment based on the anomaly severity index and the detection accuracy.

[0013] In some embodiments of this application, the calculation module is configured to: determine an adjustment coefficient based on the current time period, road segment, and weather of the vehicle; map the anomaly type, the anomaly impact range, and the anomaly persistence information to a second anomaly score; and calculate a weighted sum of the second anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information based on the adjustment coefficient to obtain the anomaly severity index.

[0014] In some embodiments of this application, the preparation module includes a preparation generation unit, configured to: acquire abnormal description information of the road abnormality, the abnormal description information including basic abnormal information and additional abnormal information, the basic abnormal information including at least the abnormal location and abnormal type, the additional abnormal information including at least one of abnormal severity index and detection accuracy; detect the abnormal avoidance processing information of the vehicle in response to the road abnormality; and generate abnormal warning information including the abnormal description information and the abnormal avoidance processing information.

[0015] According to one embodiment of this application, a road anomaly early warning method is applied to roadside equipment. The method includes: receiving anomaly early warning information reported by an on-board device, wherein the anomaly early warning information is reported by the on-board device based on an anomaly severity index, wherein the anomaly severity index is calculated based on the anomaly type, the scope of the anomaly's impact, and the anomaly's persistence information; generating anomaly sharing information based on the anomaly early warning information; and sharing the anomaly sharing information between vehicles and the road.

[0016] In some embodiments of this application, sharing the anomaly information via vehicle-to-infrastructure (V2I) includes: for road anomalies indicated in the anomaly warning information, detecting roadside devices to be confirmed from other roadside devices connected to the roadside device, wherein the roadside devices to be confirmed are other roadside devices within the coverage area that have the potential for vehicles to encounter the road anomaly; obtaining lane turning conditions, relative positions of road anomalies, and traffic volume for lanes within the coverage area of ​​the roadside devices to be confirmed; analyzing the lane turning conditions and relative positions of road anomalies to obtain the probability of vehicles in the lane entering the road anomaly; calculating the correlation index between the roadside device to be confirmed and the road anomaly based on the probability of passage and the traffic volume; confirming whether the roadside device to be confirmed is an affected roadside device based on the correlation index, wherein the affected roadside device refers to other roadside devices among the other roadside devices that are affected by the road anomaly; and sending the anomaly sharing information to the affected roadside device.

[0017] In some embodiments of this application, the step of sharing the abnormal information via vehicle-to-infrastructure (V2I) includes: performing abnormal notification condition detection on the road abnormality to obtain notification condition information of the roadside device for the road abnormality; broadcasting the abnormal sharing information to vehicles within the coverage area of ​​the roadside device according to the notification condition information; wherein, the notification condition information includes one or more of location verification information, accuracy verification information, and broadcast processing information; the step of performing abnormal notification condition detection on the road abnormality to obtain notification condition information of the roadside device for the road abnormality includes one or more of the following methods: detecting whether the road abnormality is within the coverage area of ​​the roadside device according to the abnormality warning information to obtain location verification information; performing secondary detection on the road abnormality through a road abnormality detection device to obtain accuracy verification information of the road abnormality; analyzing the broadcast range and broadcast duration of the road abnormality according to the abnormality warning information to obtain broadcast processing information.

[0018] According to one embodiment of this application, a road anomaly early warning device is applied to roadside equipment. The device includes: a receiving module for receiving anomaly early warning information reported by an on-board device, wherein the anomaly early warning information is reported by the on-board device based on an anomaly severity index, the anomaly severity index being calculated based on the anomaly type, the anomaly impact range, and the anomaly persistence information; a generating module for generating anomaly sharing information based on the anomaly early warning information; and a sharing module for sharing the anomaly sharing information between vehicles and the road.

[0019] In some embodiments of this application, the sharing module is configured to: detect, in response to road anomalies indicated in the anomaly warning information, from other roadside devices connected to the roadside device, identify roadside devices to be confirmed, wherein the roadside devices to be confirmed are other roadside devices within the coverage area that have the potential to cause vehicles to encounter the road anomaly; acquire lane turning conditions, relative positions of road anomalies, and traffic volume for lanes within the coverage area of ​​the roadside devices to be confirmed; analyze the lane turning conditions and relative positions of road anomalies to obtain the probability of vehicles in the lane entering the road anomaly; calculate the correlation index between the roadside device to be confirmed and the road anomaly based on the probability of passage and the traffic volume; confirm whether the roadside device to be confirmed is an affected roadside device based on the correlation index, wherein the affected roadside device refers to other roadside devices among the other roadside devices that are affected by the road anomaly; and send the anomaly sharing information to the affected roadside device.

[0020] In some embodiments of this application, the sharing module is configured to: perform anomaly notification condition detection on the road anomaly to obtain notification condition information of the roadside device for the road anomaly; broadcast the anomaly sharing information to vehicles within the coverage area of ​​the roadside device according to the notification condition information; wherein, the notification condition information includes one or more of location verification information, accuracy verification information, and broadcast processing information; the sharing module is configured to implement one or more of the following methods: detect whether the road anomaly is within the coverage area of ​​the roadside device according to the anomaly warning information to obtain location verification information; perform secondary detection on the road anomaly through a road anomaly detection device to obtain accuracy verification information of the road anomaly; and analyze the broadcast range and broadcast duration of the road anomaly according to the anomaly warning information to obtain broadcast processing information.

[0021] According to another embodiment of this application, a storage medium stores a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the methods described in the embodiments of this application.

[0022] According to another embodiment of this application, an electronic device may include: a memory storing a computer program; and a processor reading the computer program stored in the memory to execute the methods described in the embodiments of this application.

[0023] According to another embodiment of this application, a computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described in the embodiments of this application.

[0024] In this embodiment of the application, the vehicle-mounted device can: detect the type, scope, and duration of road anomalies on the road on which the vehicle is traveling; calculate an anomaly severity index based on the anomaly type, scope, and duration; generate an anomaly warning message corresponding to the road anomaly in response to the detection of the road anomaly, and detect roadside equipment; determine whether to report the anomaly warning message to the detected roadside equipment based on the anomaly severity index; if so, report the anomaly warning message to the roadside equipment.

[0025] In this way, after the onboard equipment detects a road anomaly, it calculates an anomaly severity index based on the anomaly type, impact range, and duration. The severity index then determines whether to report an anomaly warning to the detected roadside equipment. If so, the warning is sent. This approach accurately considers the severity of road anomalies for vehicle-road cooperative anomaly warnings, effectively avoiding the drawbacks of directly sharing road anomaly information with roadside equipment. It also avoids ineffective warnings and unnecessary increases in workload for both onboard and roadside equipment. Ultimately, this significantly improves the reliability of road warnings in vehicle-road cooperative scenarios and effectively reduces the vehicle-road cooperative workload for both onboard and roadside equipment. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart of a road anomaly warning method according to an embodiment of this application is shown.

[0028] Figure 2 A flowchart of a road anomaly warning method according to another embodiment of this application is shown.

[0029] Figure 3 A block diagram of a road anomaly warning device according to an embodiment of this application is shown.

[0030] Figure 4 A block diagram of a road anomaly warning device according to another embodiment of this application is shown.

[0031] Figure 5 A block diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0032] The present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments provided herein are merely illustrative of the present disclosure and are not intended to limit the present disclosure. Furthermore, the embodiments provided below are some embodiments for implementing the present disclosure, and not all embodiments for implementing the present disclosure. Unless otherwise specified, the technical solutions described in the embodiments of the present disclosure can be implemented in any combination.

[0033] It should be noted that, in the embodiments of this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a method or apparatus that includes a list of elements includes not only the elements expressly described, but also other elements not expressly listed, or elements inherent to implementing the method or apparatus. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other related elements (e.g., steps in the method or units in the apparatus, such as portions of circuitry, processors, programs, or software, etc.) in the method or apparatus that includes that element.

[0034] For example, the road anomaly warning method provided in this disclosure includes a series of steps, but the road anomaly warning method provided in this disclosure is not limited to the steps described. Similarly, the road anomaly warning device provided in this disclosure includes a series of units, but the device provided in this disclosure is not limited to the units explicitly described, but may also include units that need to be set up for obtaining relevant information or processing based on the information.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure.

[0036] Figure 1 A flowchart illustrating a road anomaly warning method according to an embodiment of this application is shown schematically. The implementer of this road anomaly warning method can be an on-board device in a vehicle. Figure 1 As shown, the road anomaly warning method may include steps S110 to S150.

[0037] Step S110: Detect the type, scope, and duration of road anomalies on the road the vehicle is traveling on; Step S120: Calculate an anomaly severity index based on the anomaly type, scope, and duration; Step S130: In response to the detected road anomaly, generate an anomaly warning message corresponding to the road anomaly and detect roadside equipment; Step S140: Determine whether to report the anomaly warning message to the detected roadside equipment based on the anomaly severity index; Step S150: If yes, report the anomaly warning message to the roadside equipment.

[0038] While a vehicle is traveling on the road, onboard equipment can detect road anomalies using cameras, radar, and other devices. These anomalies include, but are not limited to, obstacles, potholes, traffic accidents, vehicles or pedestrians entering the road illegally, abnormal vehicle behavior, and driver fatigue. The onboard equipment can further detect the specific type of anomaly, its impact range, and its duration.

[0039] Anomalies can be categorized into different types, such as obstacles, potholes, traffic accidents, vehicles or pedestrians running over, abnormal vehicle behavior, and driver fatigue. Obstacles can be categorized by size or shape (e.g., round or rectangular). Abnormal vehicle behavior can be categorized by S-shaped driving or driving in the wrong direction. Traffic accidents can be categorized by the size of the area they encroach on. Anomalies can be identified by analyzing detected video images and radar data.

[0040] The scope of an anomaly's impact can be the physical area affected by the road anomaly within a lane. For example, the scope of an anomaly's impact could include a section of the road affecting this lane, the entire section affecting this lane but unaffected adjacent lanes, affecting adjacent lanes, or affecting all lanes. The scope of an anomaly's impact can be determined based on the vehicle congestion situation on each detected road segment.

[0041] Anomaly persistence information can refer to the duration of the detected road anomaly. This persistence can be categorized based on the specific circumstances; for example, it can be classified as long-term, medium-term, or short-term. The persistence of anomaly persistence can be determined by whether someone is addressing the anomaly, the type of anomaly, and one or more road segment types. For instance, if an animal wanders into the lane but is not being led, the persistence can be classified as medium-term; if an animal wanders into the lane but is being led, the persistence can be classified as short-term.

[0042] According to the predetermined calculation method, the severity index of the road abnormality is calculated based on the abnormality type, the scope of the abnormality, and the information on the duration of the abnormality. The severity index of the road abnormality can reflect the severity of the abnormality. The higher the severity index of the road abnormality, the more severe the abnormality.

[0043] In response to the road anomaly, an anomaly warning message can be generated. The system can detect whether the road is within the coverage area of ​​a roadside unit (RSU) by checking for message broadcasts from RSUs, thus identifying the detected RSUs. Furthermore, based on the anomaly severity index, it can determine whether to report an anomaly warning message to the detected RSUs. If so, an anomaly warning message is reported to the RSUs; otherwise, it is not.

[0044] In this manner, based on steps S110 to S150, after the on-board equipment detects a road anomaly, it calculates an anomaly severity index based on the anomaly type, impact range, and duration. The severity index determines whether to report an anomaly warning to the detected roadside equipment. If so, the warning is reported. This approach accurately considers the severity of road anomalies for vehicle-road cooperative anomaly warnings, effectively avoiding the drawbacks of directly sharing road anomaly information with roadside equipment. It also avoids invalid warnings and unnecessary increases in the workload of both on-board and roadside equipment. Therefore, it effectively improves the reliability of road warnings in vehicle-road cooperative road warning scenarios and effectively reduces the vehicle-road cooperative workload of both on-board and roadside equipment.

[0045] The following description Figure 1 Further optional specific embodiments for each step performed when issuing a road anomaly warning under the example.

[0046] In one embodiment, the step of calculating the anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information includes:

[0047] The anomaly type, the anomaly impact range, and the anomaly persistence information are each mapped to a first anomaly score; the anomaly severity index is obtained by multiplying the first anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information.

[0048] According to a preset first mapping table, the anomaly type can be mapped to a corresponding first anomaly score, the anomaly impact range can be mapped to a corresponding first anomaly score, and the anomaly persistence information can be mapped to a corresponding first anomaly score. Further, the three first anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information are multiplied together; the product is the anomaly severity index obtained in this embodiment. This anomaly severity index can comprehensively and effectively reflect the severity caused by the anomaly type, the anomaly impact range, and the anomaly persistence information of a road anomaly.

[0049] For example, the first mapping table can store: two anomaly types, "S-shaped driving" and "cow running into the road", two anomaly impact ranges, "the entire road segment" and "a small section of the road", and two anomaly persistence information, "long time" and "short time". The first anomaly score corresponding to "S-shaped driving" is 50, the first anomaly score corresponding to "cow running into the road" is 45, the first anomaly score corresponding to "the entire road segment" is 90, the first anomaly score corresponding to "a small section of the road" is 40, the first anomaly score corresponding to "long time" is 70, and the first anomaly score corresponding to "short time" is 20.

[0050] Furthermore, if the anomaly type, impact range, and duration of the road anomaly are "S-shaped driving," "entire road section," and "long duration," respectively, then "S-shaped driving," "entire road section," and "long duration" can be mapped to first anomaly scores of 50, 90, and 70, respectively, and multiplied together to obtain an anomaly severity index of 315,000. If the anomaly type, impact range, and duration of the road anomaly are "cattle intrusion," "small road section," and "short duration," then "cattle intrusion," "small road section," and "short duration" can be mapped to first anomaly scores of 45, 40, and 20, respectively, and multiplied together to obtain an anomaly severity index of 36,000. It is evident that different road anomalies correspond to different anomaly severity indices, and the same road anomaly will also have different anomaly severity indices at different times and distances. Based on the anomaly severity index, a reliable decision can be made on when to effectively report anomaly warning information to roadside equipment.

[0051] In one embodiment, the step of calculating the anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information includes:

[0052] An adjustment coefficient is determined based on the current time period, road segment, and weather conditions of the vehicle; the anomaly type, the anomaly impact range, and the anomaly persistence information are respectively mapped to a second anomaly score; based on the adjustment coefficient, a weighted sum of the second anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information is calculated to obtain the anomaly severity index.

[0053] According to the preset second mapping table, the anomaly type can be mapped to the corresponding second anomaly score, the anomaly impact range can be mapped to the corresponding second anomaly score, and the anomaly persistence information can be mapped to the corresponding second anomaly score.

[0054] For example, the second mapping table can also store: two anomaly types, "S-shaped driving" and "cow intrusion"; two anomaly impact ranges, "entire road segment" and "small road segment"; and two anomaly persistence information, "long duration" and "short duration". The second anomaly score corresponding to "S-shaped driving" is 50, "cow intrusion" is 45, "entire road segment" is 90, "small road segment" is 40, "long duration" is 70, and "short duration" is 20. Furthermore, if the anomaly type, impact range, and persistence information of a road anomaly are "S-shaped driving", "entire road segment", and "long duration" respectively, then "S-shaped driving", "entire road segment", and "long duration" can be mapped to second anomaly scores of 50, 90, and 70 respectively, and so on, allowing for corresponding mappings for different information.

[0055] Furthermore, the current time period, road segment, and weather conditions of the vehicle can be determined. The time period is such as the morning rush hour from 6:00 to 9:00 or from 12:00 to 14:00. The time period can be determined based on the current time. The specific road segment is such as urban road segment or rural road segment. The road segment can be obtained from navigation maps. The weather is such as rain or sunny. The weather can be obtained from connected devices or the vehicle's own equipment.

[0056] Based on the preset adjustment coefficient table, the adjustment coefficients corresponding to the current time period, road segment, and weather conditions of the vehicle can be determined. The adjustment coefficients can include the adjustment coefficients corresponding to the anomaly type, the scope of the anomaly's impact, and the anomaly's duration.

[0057] The weighted sum of the second anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information can be calculated as follows: multiply the second anomaly score corresponding to the anomaly type by the adjustment system to obtain a first result; multiply the second anomaly score corresponding to the anomaly impact range by the adjustment system to obtain a second result; multiply the second anomaly score corresponding to the anomaly persistence information by the adjustment system to obtain a third result; and add the first, second, and third results to obtain the anomaly severity index.

[0058] This anomaly severity index further considers the impact of different time periods, road sections, and weather conditions. This anomaly severity index can more accurately reflect the anomaly type, the scope of the anomaly's impact, and the severity caused by the anomaly's persistence information.

[0059] In one embodiment, generating the abnormal warning information corresponding to the road abnormality includes: obtaining abnormal description information of the road abnormality, the abnormal description information including basic abnormal information and additional abnormal information, the basic abnormal information including at least the abnormal location and abnormal type, and the additional abnormal information including at least one of abnormal severity index and detection accuracy; generating abnormal warning information including the abnormal description information.

[0060] The anomaly severity index can be calculated using any of the aforementioned embodiments, and the detection accuracy can be obtained through simultaneous analysis during road anomaly detection. Anomaly warning information, including anomaly description information, is generated. This warning information reliably describes the road anomaly, improving the effectiveness of road warnings.

[0061] It is understood that in other embodiments, generating the abnormal warning information corresponding to the road abnormality may include: obtaining abnormal description information of the road abnormality, the abnormal description information including basic abnormal information, the basic abnormal information including at least the abnormal location and abnormal type; generating abnormal warning information including the abnormal description information.

[0062] Furthermore, in one embodiment, generating the abnormal warning information including the abnormal description information includes: detecting the vehicle's abnormal avoidance processing information in response to the road abnormality; and generating the abnormal warning information including the abnormal description information and the abnormal avoidance processing information.

[0063] By combining real-time vehicle location, direction of travel, and speed information, it can be determined whether the vehicle's passage is affected. If so, the system can alert the driver to take evasive action or activate the vehicle's autonomous driving program to avoid the obstacle. Furthermore, after detecting road anomalies, warnings can be played in the vehicle to alert the driver; drivers may also visually detect road anomalies and take evasive action themselves.

[0064] The system records the vehicle's evasive maneuvers, obtaining abnormal evasion handling information. This information includes lane change information (which could be the new lane), deceleration information (which could be the speed at which the vehicle decelerates), acceleration information (which could be the speed at which the vehicle accelerates), and stopping to avoid the obstacle. Furthermore, it can generate abnormal warning information that includes both anomaly description and abnormal evasion handling information, further enhancing the effectiveness of road warnings.

[0065] It is understood that in other embodiments, generating abnormal warning information that includes the abnormal description information may be generating abnormal warning information that only includes the abnormal description information.

[0066] In one embodiment, determining whether to report the abnormal warning information to the roadside device based on the abnormality severity index includes: determining the detection duration of the roadside device; and determining whether to report the abnormal warning information to the roadside device based on the abnormality severity index and the detection duration.

[0067] The detection duration can be obtained by calculating the difference between the scheduled start time and the time when the roadside equipment is detected. The scheduled start time can be the time when the road abnormality is detected or the time when the roadside equipment is started to detect the abnormality.

[0068] A very short detection time indicates that the vehicle is currently within the coverage area of ​​the roadside equipment. A longer detection time indicates that the vehicle traveled for some time before entering the coverage area of ​​the roadside equipment.

[0069] Based on the severity index and detection duration, it can be determined whether to report abnormal warning information to roadside equipment, which can further avoid invalid warnings caused by invalid reporting.

[0070] In one embodiment, determining whether to report the abnormal warning information to the roadside equipment based on the abnormality severity index and the detection duration can specifically involve: determining the severity level corresponding to the abnormality severity index, where a higher abnormality severity index corresponds to a higher severity level; determining the corresponding duration range of the detection duration; and determining whether to report the abnormal warning information to the roadside equipment based on the severity level and the duration range.

[0071] In one example, the severity level may include one of a first level, a second level, and a third level, with the first level, second level, and third level increasing sequentially. Determining whether to report the abnormal warning information to the roadside device based on the severity level and the duration range can specifically be as follows: if the detection duration is less than a first duration threshold, then directly determine to report the abnormal warning information to the roadside device; if the detection duration is greater than the first duration threshold but less than the second duration, and the severity level is a first level, a second level, or a third level, then determine to report the abnormal warning information to the roadside device; if the detection duration is greater than the second duration threshold, and the severity level is a second level or a third level, then determine to report the abnormal warning information to the roadside device; if the detection duration is greater than the third duration threshold, and the severity level is a third level, then determine to report the abnormal warning information to the roadside device.

[0072] It is understood that in other implementations, other strategies can be used to determine whether to report the abnormal warning information to the roadside equipment based on the abnormality severity index and the detection duration. For example, if the abnormality severity index is greater than the first index and the detection duration is less than the predetermined duration, then it is determined to report the abnormal warning information to the roadside equipment; otherwise, it is not reported.

[0073] In one embodiment, step S140, determining whether to report the abnormality warning information to the detected roadside equipment based on the abnormality severity index, includes: continuously detecting the road abnormality and determining the detection accuracy of the road abnormality during the vehicle's operation; and determining whether to report the abnormality warning information to the detected roadside equipment based on the abnormality severity index and the detection accuracy.

[0074] As vehicles continuously travel on the road, onboard equipment can continuously detect road anomalies and determine the accuracy of the detection. Generally, the closer the vehicle is to the road anomaly and the fewer vehicles are obstructing it, the higher the detection accuracy.

[0075] The on-board equipment determines whether to report abnormal warning information to the roadside equipment based on the severity index of the abnormality and the detection accuracy. This can further avoid inaccurate or inadequate reporting and can also further enhance the effectiveness of abnormal warnings.

[0076] Specifically, the decision to report the abnormal warning information to the roadside equipment is based on the severity index and the detection accuracy. Specifically, if the severity index is greater than the second index and the detection accuracy is higher than the predetermined accuracy, then the abnormal warning information is reported to the roadside equipment.

[0077] For example, if the second index is 50,000 and the predetermined accuracy is 60%, and the severity index of the road anomaly is 36,000 with a detection accuracy of 90%, then the severity index of 36,000 is less than the second index of 50,000, and therefore no anomaly warning information will be reported to the roadside equipment. Furthermore, if the severity index of the road anomaly is 315,000 with a detection accuracy of 65%, then the detection accuracy of 65% is greater than the predetermined accuracy of 60%, and the severity index of 315,000 is greater than the second index of 50,000. Although the detection accuracy of 65% is low, it exceeds the predetermined accuracy, and the road anomaly is relatively severe, so an anomaly warning information will be reported to the roadside equipment.

[0078] Alternatively, based on the severity index and detection accuracy, it can be determined whether to report the abnormal warning information to the roadside equipment. Specifically, if the product of the severity index and the detection accuracy is greater than a predetermined product, then it can be determined whether to report the abnormal warning information to the roadside equipment.

[0079] For example, if the predetermined product is 30000, and the severity index of the road anomaly is 36000 and the detection accuracy is 90%, then the product of the severity index 36000 and the detection accuracy 90% is 32400. Since 32400 is greater than the predetermined product 30000, in this embodiment, the anomaly warning information will still be reported to the roadside equipment.

[0080] Figure 2 A flowchart illustrating another embodiment of a road anomaly warning method according to this application is shown schematically. The implementer of this road anomaly warning method may be a roadside device. Figure 2 As shown, the road anomaly warning method may include steps S210 to S230.

[0081] Step S210: Receive abnormal warning information reported by the vehicle-mounted device. The abnormal warning information is reported by the vehicle-mounted device based on the abnormality severity index, which is calculated based on the abnormality type, the scope of the abnormality, and the duration of the abnormality. Step S220: Generate abnormality sharing information based on the abnormal warning information. Step S230: Share the abnormality sharing information between the vehicle and the road.

[0082] While a vehicle is traveling on the road, onboard equipment can detect road anomalies using cameras, radar, and other devices. These anomalies include, but are not limited to, obstacles, potholes, traffic accidents, vehicles or pedestrians entering the road illegally, abnormal vehicle behavior, and driver fatigue. The onboard equipment can further detect the specific type of anomaly, its impact range, and its duration.

[0083] Anomalies can be categorized into different types, such as obstacles, potholes, traffic accidents, vehicles or pedestrians running over, abnormal vehicle behavior, and driver fatigue. Obstacles can be categorized by size or shape (e.g., round or rectangular). Abnormal vehicle behavior can be categorized by S-shaped driving or driving in the wrong direction. Traffic accidents can be categorized by the size of the area they encroach on. Anomalies can be identified by analyzing detected video images and radar data.

[0084] The scope of an anomaly's impact can be the physical area affected by the road anomaly within a lane. For example, the scope of an anomaly's impact could include a section of the road affecting this lane, the entire section affecting this lane but unaffected adjacent lanes, affecting adjacent lanes, or affecting all lanes. The scope of an anomaly's impact can be determined based on the vehicle congestion situation on each detected road segment.

[0085] Anomaly persistence information can refer to the duration of the detected road anomaly. This persistence can be categorized based on the specific circumstances; for example, it can be classified as long-term, medium-term, or short-term. The persistence of anomaly persistence can be determined by whether someone is addressing the anomaly, the type of anomaly, and one or more road segment types. For instance, if an animal wanders into the lane but is not being led, the persistence can be classified as medium-term; if an animal wanders into the lane but is being led, the persistence can be classified as short-term.

[0086] According to the predetermined calculation method, the severity index of the road abnormality is calculated based on the abnormality type, the scope of the abnormality, and the information on the duration of the abnormality. The severity index of the road abnormality can reflect the severity of the abnormality. The higher the severity index of the road abnormality, the more severe the abnormality.

[0087] In response to the road anomaly, an anomaly warning message can be generated. The system can detect whether the road is within the coverage area of ​​a roadside unit (RSU) by checking for message broadcasts from RSUs, thus identifying the detected RSUs. Furthermore, based on the anomaly severity index, it can determine whether to report an anomaly warning message to the detected RSUs. If so, an anomaly warning message is reported to the RSUs; otherwise, it is not.

[0088] Roadside equipment can generate anomaly sharing information based on received anomaly warning information and share this information with vehicles, thereby achieving large-scale road anomaly warning. The anomaly sharing information can include relevant information from the anomaly warning information (such as anomaly location, anomaly type, etc.) and avoidance suggestions generated by the roadside equipment based on anomaly avoidance handling information from the anomaly warning information (such as speed, lane suggestions, route suggestions, etc.).

[0089] In this manner, based on steps S210 to S230, after the on-board equipment detects a road anomaly, it calculates an anomaly severity index based on the anomaly type, impact range, and duration. The severity index determines whether to report an anomaly warning to the detected roadside equipment. If so, the warning is reported. This approach accurately considers the severity of road anomalies for vehicle-road cooperative anomaly warnings, effectively avoiding the drawbacks of directly sharing road anomaly information with roadside equipment. It also avoids invalid warnings and unnecessary increases in the workload of both on-board and roadside equipment. Therefore, it effectively improves the reliability of road warnings in vehicle-road cooperative road warning scenarios and effectively reduces the vehicle-road cooperative workload of both on-board and roadside equipment.

[0090] The following description Figure 2 Further optional specific embodiments for each step performed when issuing a road anomaly warning under the example.

[0091] In one embodiment, the abnormal sharing information is shared between vehicles and the road, including at least one of the following methods:

[0092] The abnormal sharing information is broadcast to vehicles within the coverage area of ​​the roadside equipment;

[0093] The abnormal sharing information is sent to other roadside devices connected to the roadside device.

[0094] The device that sends the abnormal sharing information to the designated road management department or designated road management personnel.

[0095] The abnormal information is broadcast to vehicles within the coverage area of ​​the roadside equipment. As a result, vehicles entering the coverage area of ​​the roadside equipment can receive timely warnings of road anomalies, especially vehicles that do not have the ability to detect road anomalies.

[0096] The abnormality information is shared with other roadside devices connected to the roadside equipment. These other roadside devices can then issue warnings to vehicles within the corresponding range, further indicating the scope of the road abnormality warning.

[0097] The device sends abnormality information to the designated road management department or designated road management personnel, who can then promptly carry out road abnormality repairs.

[0098] In one embodiment, sending the abnormality sharing information to other roadside devices connected to the roadside device includes:

[0099] In response to the road anomaly indicated in the anomaly warning information, the affected roadside equipment is detected, where the affected roadside equipment refers to other roadside equipment affected by the road anomaly; the anomaly sharing information is sent to the affected roadside equipment.

[0100] By further detecting the affected roadside devices from other roadside devices connected to the roadside equipment, and only sending the abnormality sharing information to the affected roadside devices, invalid road warnings for other unaffected roadside devices can be avoided. At the same time, the load on the shared information of roadside devices can be reliably avoided, further enhancing the reliability of road abnormality warnings.

[0101] In one embodiment, the affected roadside device for detecting the road anomaly includes: detecting a roadside device to be confirmed from the other roadside devices, wherein the roadside device to be confirmed is one of the other roadside devices within its coverage area that has the potential for vehicles to encounter the road anomaly; acquiring lane turning information, relative location of the road anomaly, and traffic volume for the lanes within the coverage area of ​​the roadside device to be confirmed; analyzing the lane turning information and relative location of the road anomaly to obtain the probability of a vehicle in the lane entering the road anomaly; calculating a correlation index between the roadside device to be confirmed and the road anomaly based on the probability of passage and the traffic volume; and confirming whether the roadside device to be confirmed is the affected roadside device based on the correlation index.

[0102] From other roadside devices connected to the roadside equipment, the first step is to detect roadside devices to be identified. These are roadside devices within their coverage area that have the potential to cause vehicles to encounter road anomalies. In other words, if a vehicle within the coverage area of ​​a particular roadside device has the potential to encounter a road anomaly, then that particular roadside device can be identified as a roadside device to be identified. For example, a roadside device to be identified is one located ahead of the detected roadside device and at a distance less than a predetermined distance from the detected roadside device, according to the vehicle's direction of travel.

[0103] From the roadside equipment to be confirmed, we can obtain the lane turning status, relative location of road anomalies, and traffic volume of the lanes within the coverage area of ​​the roadside equipment to be confirmed. The lane turning status refers to the turning status set on the lane, the relative location of road anomalies refers to the relative distance between the lane and the abnormal location of the road anomaly, and the traffic volume refers to the traffic volume of vehicles on the lane.

[0104] Based on the lane turning situation and the relative position of the road anomaly, an analysis can be performed to obtain the probability of a vehicle entering the road anomaly within the lane. For example, a preset probability corresponding to both the lane turning situation and the relative position of the road anomaly can be found in a probability calculation mapping table as the probability of a vehicle entering the road anomaly within the lane. It can be understood that, in other methods, this probability can also be calculated based on the mapping value of the lane turning situation and the relative position of the road anomaly using a preset function.

[0105] Based on the probability of passage and the volume of traffic, a correlation index between the roadside equipment to be identified and the road anomaly can be obtained. Specifically, the probability of passage and the volume of traffic can be multiplied to obtain the correlation index. It is understandable that other methods can be used to calculate the correlation coefficient; for example, the probability of passage and the normalized values ​​of the traffic volume within a certain range can be added together to obtain the correlation coefficient.

[0106] In this way, the correlation index can accurately reflect the degree of correlation between the roadside equipment to be identified and the road anomaly. Based on the correlation index, it can be further confirmed whether the roadside equipment to be identified is the affected roadside equipment.

[0107] In one embodiment, determining whether the roadside device to be confirmed is the affected roadside device based on the correlation index includes one of the following methods:

[0108] The first method involves determining whether the roadside device to be confirmed is the affected roadside device based on the magnitude of the correlation index and the predetermined index threshold.

[0109] The second method involves determining whether the roadside device to be confirmed is the affected roadside device based on the correlation index and the anomaly severity index in the anomaly warning information.

[0110] In the first approach, the roadside device to be confirmed is determined solely based on the correlation index. Specifically, the correlation index can be compared with a predetermined index threshold. If the correlation index is greater than the predetermined index threshold, the roadside device to be confirmed can be reliably confirmed as an affected roadside device.

[0111] In the second approach, the correlation index and the severity index of the anomaly warning information are further combined to determine whether the roadside equipment to be confirmed is the affected roadside equipment, thereby further improving the reliability of the determination of the affected roadside equipment.

[0112] Specifically, determining whether the roadside device to be confirmed is the affected roadside device based on the correlation index and the anomaly severity index in the anomaly warning information can be achieved by: determining the correlation level corresponding to the correlation index and the severity level corresponding to the anomaly severity index; and determining whether the roadside device to be confirmed is the affected roadside device based on the correlation level and the severity level.

[0113] For example, the correlation index is divided into four correlation levels: D1, D2, D3, and D4; D1, D2, D3, and D4 increase sequentially, and the higher the correlation index, the higher the correlation level; the severity level can include one of the first, second, and third levels, with the first, second, and third levels increasing sequentially; the confirmation information corresponding to both the correlation level and the severity level can be queried from the preset level verification table. The confirmation information can include yes or no. If yes, then the roadside equipment to be confirmed is determined to be the affected roadside equipment.

[0114] In one embodiment, broadcasting the abnormal sharing information to vehicles within the coverage area of ​​the roadside equipment includes:

[0115] Anomaly notification condition detection is performed on the road anomaly to obtain the notification condition information of the roadside equipment for the road anomaly; according to the notification condition information, the anomaly sharing information is broadcast to vehicles located within the coverage area of ​​the roadside equipment.

[0116] In this embodiment, the abnormality notification conditions are first detected for road abnormalities to obtain the notification condition information of the roadside equipment for the road abnormality. Then, the broadcast is carried out based on the information, which can further improve the reliability of road abnormality broadcast.

[0117] It is understood that in other embodiments, abnormal notification condition detection may be omitted, and abnormal sharing information may be directly broadcast to vehicles within the coverage area of ​​the roadside equipment.

[0118] In one embodiment, the notification condition information includes one or more of location verification information, accuracy verification information, and broadcast processing information; the abnormal notification condition detection of the road abnormality to obtain the notification condition information of the roadside device for the road abnormality includes one or more of the following methods:

[0119] The first method involves detecting whether the road anomaly is within the coverage area of ​​the roadside equipment based on the anomaly warning information, thereby obtaining location verification information.

[0120] The second method involves using road anomaly detection equipment to perform secondary detection on the road anomaly, thereby obtaining accuracy verification information for the road anomaly.

[0121] The third method involves analyzing the broadcast range and duration of the road anomaly based on the abnormal warning information to obtain broadcast processing information.

[0122] In the first method, based on the abnormal location of the road abnormality in the abnormal warning information and combined with the coverage of the roadside equipment, it is possible to detect whether the abnormal location of the road abnormality is within the coverage of the roadside equipment, and thus obtain location verification information. The location verification information may include whether it is within the coverage or not.

[0123] In the second approach, roadside equipment can use connected cameras or radar to perform secondary detection and verification of road anomalies, verifying the accuracy of the road anomalies and obtaining accuracy verification information, which can include both accurate and inaccurate information.

[0124] The procedure may further include, before the road anomaly is detected a second time using the road anomaly detection device: if the anomaly warning information includes detection accuracy, determining whether to perform a second road anomaly detection using the road anomaly detection device based on the detection accuracy; if yes, then performing a second road anomaly detection using the road anomaly detection device; if no, then not performing a second road anomaly detection using the road anomaly detection device. If the anomaly warning information reported by the vehicle-mounted device includes detection accuracy, and the detection accuracy is higher than a predetermined accuracy score, then it can be determined that the road anomaly will not be detected a second time using the road anomaly detection device; if the detection accuracy is lower than the predetermined accuracy score, then it can be determined that the road anomaly will be detected a second time using the road anomaly detection device, thus avoiding redundant second detection.

[0125] The third method, which involves analyzing the broadcast range and duration of the road anomaly based on the anomaly warning information to obtain broadcast processing information, may include one of the following methods: obtaining broadcast processing information including the broadcast range and duration of the road anomaly based on the anomaly type in the anomaly warning information; or obtaining broadcast processing information including the broadcast range and duration of the road anomaly based on the anomaly severity index in the anomaly warning information.

[0126] If the anomaly warning information includes an anomaly type, the broadcast range and duration corresponding to the anomaly type can be retrieved from the preset broadcast information table based on the anomaly type as broadcast processing information. If the anomaly warning information includes an anomaly severity index, the broadcast range and duration corresponding to the anomaly severity index can be retrieved from the preset broadcast information table based on the anomaly severity index as broadcast processing information.

[0127] Furthermore, according to the notification condition information, broadcasting the abnormal sharing information to vehicles within the coverage area of ​​the roadside equipment can be done as follows: if either the location verification information is "not within range" or the accuracy information is "inaccurate," then the abnormal sharing information will not be broadcast to vehicles within the coverage area of ​​the roadside equipment. Otherwise, the abnormal sharing information can be broadcast to vehicles within the coverage area of ​​the roadside equipment. When broadcasting the abnormal sharing information to vehicles within the coverage area of ​​the roadside equipment, the abnormal sharing information can be continuously broadcast within the analyzed broadcast range and the broadcast duration. When the broadcast duration is reached, the broadcast of the abnormal sharing information will stop.

[0128] To facilitate better implementation of the road anomaly warning method provided in this application, this application also provides a road anomaly warning device based on the above-described road anomaly warning method. The meanings of the terms used are the same as in the road anomaly warning method described above, and specific implementation details can be found in the descriptions within the method embodiments. Figure 3 A block diagram of a road anomaly warning device 300 according to an embodiment of the present application is shown. The road anomaly warning device 300 can be applied to vehicle-mounted equipment.

[0129] like Figure 3 As shown, the road anomaly warning device 300 may include: a detection module 310 for detecting the anomaly type, impact range, and duration of anomalies on the road where the vehicle is traveling; a calculation module 320 for calculating an anomaly severity index based on the anomaly type, impact range, and duration; a preparation module 330 for generating an anomaly warning message corresponding to the detected road anomaly and detecting roadside equipment in response to the detection of the road anomaly; a determination module 340 for determining whether to report the anomaly warning message to the detected roadside equipment based on the anomaly severity index; and a reporting module 350 for reporting the anomaly warning message to the roadside equipment if the anomaly is detected.

[0130] In some embodiments of this application, the determining module is configured to: continuously detect road anomalies and determine the detection accuracy of the road anomalies during the vehicle's operation; and determine whether to report the anomaly warning information to the roadside equipment based on the anomaly severity index and the detection accuracy.

[0131] In some embodiments of this application, the calculation module is configured to: determine an adjustment coefficient based on the current time period, road segment, and weather of the vehicle; map the anomaly type, the anomaly impact range, and the anomaly persistence information to a second anomaly score; and calculate a weighted sum of the second anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information based on the adjustment coefficient to obtain the anomaly severity index.

[0132] In some embodiments of this application, the preparation module includes a preparation generation unit, configured to: acquire abnormal description information of the road abnormality, the abnormal description information including basic abnormal information and additional abnormal information, the basic abnormal information including at least the abnormal location and abnormal type, the additional abnormal information including at least one of abnormal severity index and detection accuracy; detect the abnormal avoidance processing information of the vehicle in response to the road abnormality; and generate abnormal warning information including the abnormal description information and the abnormal avoidance processing information.

[0133] Figure 4 A block diagram of a road anomaly warning device 400 according to another embodiment of this application is shown. This road anomaly warning device 400 can be applied to roadside equipment.

[0134] The road anomaly early warning device 400 includes: a receiving module 410 for receiving anomaly early warning information reported by an on-board device, the anomaly early warning information being reported by the on-board device based on an anomaly severity index, the anomaly severity index being calculated based on the anomaly type, anomaly impact range, and anomaly persistence information of the road anomaly; a generating module 420 for generating anomaly sharing information based on the anomaly early warning information; and a sharing module 430 for sharing the anomaly sharing information between vehicles and the road.

[0135] In some embodiments of this application, the sharing module is configured to: detect, in response to road anomalies indicated in the anomaly warning information, from other roadside devices connected to the roadside device, identify roadside devices to be confirmed, wherein the roadside devices to be confirmed are other roadside devices within the coverage area that have the potential to cause vehicles to encounter the road anomaly; acquire lane turning conditions, relative positions of road anomalies, and traffic volume for lanes within the coverage area of ​​the roadside devices to be confirmed; analyze the lane turning conditions and relative positions of road anomalies to obtain the probability of vehicles in the lane entering the road anomaly; calculate the correlation index between the roadside device to be confirmed and the road anomaly based on the probability of passage and the traffic volume; confirm whether the roadside device to be confirmed is an affected roadside device based on the correlation index, wherein the affected roadside device refers to other roadside devices among the other roadside devices that are affected by the road anomaly; and send the anomaly sharing information to the affected roadside device.

[0136] In some embodiments of this application, the sharing module is configured to: perform anomaly notification condition detection on the road anomaly to obtain notification condition information of the roadside device for the road anomaly; broadcast the anomaly sharing information to vehicles within the coverage area of ​​the roadside device according to the notification condition information; wherein, the notification condition information includes one or more of location verification information, accuracy verification information, and broadcast processing information; the sharing module is configured to implement one or more of the following methods: detect whether the road anomaly is within the coverage area of ​​the roadside device according to the anomaly warning information to obtain location verification information; perform secondary detection on the road anomaly through a road anomaly detection device to obtain accuracy verification information of the road anomaly; and analyze the broadcast range and broadcast duration of the road anomaly according to the anomaly warning information to obtain broadcast processing information.

[0137] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0138] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 5 As shown, it illustrates a block diagram of an electronic device involved in an embodiment of this application, specifically:

[0139] The electronic device may include components such as a processor 501 with one or more processing cores, a memory 502 with one or more computer-readable storage media, and a power supply 503. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0140] The processor 501 is the control center of the electronic device. It connects to various parts of the computer device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 502, and by calling data stored in the memory 502, it performs various functions of the computer device and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user page, and application programs, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 501.

[0141] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.

[0142] The electronic device also includes a power supply 503 that supplies power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0143] Specifically, in this embodiment, the processor 501 in the electronic device loads the executable files corresponding to the processes of one or more computer programs into the memory 502 according to the following instructions, and the processor 501 runs the computer programs stored in the memory 502, thereby realizing the various functions in the foregoing embodiments of this application.

[0144] If the electronic device is implemented as an in-vehicle device, the processor 501 can perform the following steps: detect the anomaly type, the scope of the anomaly's impact, and the duration of the anomaly on the road on which the vehicle is traveling; calculate an anomaly severity index based on the anomaly type, the scope of the anomaly's impact, and the duration of the anomaly; in response to detecting the road anomaly, generate an anomaly warning message corresponding to the road anomaly, and detect roadside equipment; determine whether to report the anomaly warning message to the detected roadside equipment based on the anomaly severity index; if so, report the anomaly warning message to the roadside equipment.

[0145] If the electronic device is implemented as a roadside device, the processor 501 can perform the following steps: receive abnormal warning information reported by the vehicle-mounted device, the abnormal warning information being reported by the vehicle-mounted device based on an abnormality severity index, the abnormality severity index being calculated based on the abnormality type, abnormality impact range, and abnormality persistence information of the road abnormality; generate abnormality sharing information based on the abnormal warning information; and share the abnormality sharing information between the vehicle and the road.

[0146] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by a computer program, or by a computer program controlling related hardware. The computer program can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0147] Therefore, embodiments of this application also provide a storage medium storing a computer program that can be loaded by a processor to execute the steps in any of the methods provided in embodiments of this application.

[0148] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0149] Since the computer program stored in the storage medium can execute the steps of any of the methods provided in the embodiments of this application, the beneficial effects that the methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0150] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0151] It should be understood that this application is not limited to the embodiments described above and shown in the accompanying drawings, but various modifications and changes can be made without departing from its scope.

Claims

1. A method for early warning of road anomalies, characterized in that, Applied to in-vehicle equipment, the method includes: Detect the type, scope, and duration of road anomalies on the road where the vehicle is traveling; An anomaly severity index is calculated based on the anomaly type, the anomaly impact range, and the anomaly persistence information. In response to the detection of the road anomaly, an anomaly warning message corresponding to the road anomaly is generated, and roadside equipment is detected; Determine whether to report the abnormality warning information to the detected roadside equipment based on the abnormality severity index; If so, the abnormal warning information shall be reported to the roadside equipment; The calculation of the anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information includes: The adjustment coefficient is determined based on the current time period, road segment, and weather conditions of the vehicle. The anomaly type, the scope of the anomaly's impact, and the anomaly's persistence information are each mapped to a second anomaly score; Based on the adjustment coefficient, the weighted sum of the second anomaly score corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information is calculated to obtain the anomaly severity index.

2. The method according to claim 1, characterized in that, The step of determining whether to report the abnormal warning information to the detected roadside equipment based on the abnormality severity index includes: During the vehicle's operation, road anomalies are continuously detected, and the accuracy of the road anomaly detection is determined. Based on the severity index of the anomaly and the detection accuracy, determine whether to report the anomaly warning information to the detected roadside equipment.

3. The method according to claim 1, characterized in that, The generation of abnormal warning information corresponding to the road abnormality includes: Obtain the abnormal description information of the road abnormality. The abnormal description information includes basic abnormal information and additional abnormal information. The basic abnormal information includes at least the abnormal location and abnormal type. The additional abnormal information includes at least one of abnormal severity index and detection accuracy. Detect the vehicle's unusual avoidance handling information in response to the road anomaly; Generate an anomaly warning message that includes the anomaly description information and the anomaly avoidance and handling information.

4. A method for early warning of road anomalies, characterized in that, Applied to roadside equipment, the method includes: The system receives abnormal warning information reported by an on-board device. This abnormal warning information is reported by the on-board device based on an abnormality severity index, which is calculated based on the abnormality type, the scope of impact, and the duration of the abnormality. The calculation method for the abnormality severity index includes: determining an adjustment coefficient based on the vehicle's current time period, road segment, and weather; mapping the abnormality type, the scope of impact, and the duration of the abnormality to a second abnormality score; and calculating a weighted sum of the second abnormality scores corresponding to the abnormality type, the scope of impact, and the duration of the abnormality based on the adjustment coefficient to obtain the abnormality severity index. Anomaly sharing information is generated based on the aforementioned anomaly warning information; The abnormal sharing information will be shared between vehicles and the road.

5. The method according to claim 4, characterized in that, The step of sharing the abnormal shared information via vehicle-to-infrastructure includes: In response to the road anomaly indicated in the anomaly warning information, other roadside devices connected to the roadside device are detected to be confirmed. The other roadside devices within the coverage area that have the potential to be encountered by vehicles due to the road anomaly are the roadside devices to be confirmed. The system obtains the lane turning status, relative location of road anomalies, and traffic volume of the lanes within the coverage area of ​​the roadside equipment to be confirmed. Based on the analysis of the lane turning situation and the relative position of the road anomaly, the probability of a vehicle in the lane entering the road anomaly is obtained; The correlation index between the roadside equipment to be confirmed and the road anomaly is calculated based on the probability of passage and the traffic volume. Based on the correlation index, it is determined whether the roadside equipment to be confirmed is an affected roadside equipment. The affected roadside equipment refers to other roadside equipment among the other roadside equipment that is affected by the road anomaly. The abnormal sharing information is sent to the affected roadside equipment.

6. The method according to claim 4, characterized in that, The step of sharing the abnormal shared information via vehicle-to-infrastructure includes: Anomaly notification condition detection is performed on the road anomaly to obtain the notification condition information of the roadside equipment for the road anomaly; Based on the notification conditions, the abnormal sharing information is broadcast to vehicles within the coverage area of ​​the roadside equipment; The notification condition information includes one or more of location verification information, accuracy verification information, and broadcast processing information; the abnormal notification condition detection of the road abnormality to obtain the notification condition information of the roadside device for the road abnormality includes one or more of the following methods: Based on the abnormal warning information, detect whether the road abnormality is within the coverage area of ​​the roadside equipment to obtain location verification information; The road anomaly is further detected by a road anomaly detection device to obtain accuracy verification information for the road anomaly. Based on the abnormal warning information, the broadcast range and duration of the road abnormality are analyzed to obtain broadcast processing information.

7. A road anomaly early warning device, characterized in that, Applied to vehicle-mounted equipment, the device includes: The detection module is used to detect the type of road anomaly, the scope of its impact, and the duration of the anomaly on the road where the vehicle is traveling. The calculation module is used to calculate an anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information. The calculation of the anomaly severity index based on the anomaly type, the anomaly impact range, and the anomaly persistence information includes: determining an adjustment coefficient based on the vehicle's current time period, road segment, and weather; mapping the anomaly type, the anomaly impact range, and the anomaly persistence information to second anomaly scores respectively; and calculating a weighted sum of the second anomaly scores corresponding to the anomaly type, the anomaly impact range, and the anomaly persistence information based on the adjustment coefficient to obtain the anomaly severity index. The preparation module is used to generate an abnormality warning information corresponding to the road abnormality in response to the detection of the road abnormality, and to detect roadside equipment; The determination module is used to determine whether to report the abnormality warning information to the detected roadside equipment based on the abnormality severity index; The reporting module is used to report the abnormal warning information to the roadside equipment if the condition is met.

8. A storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the method described in any one of claims 1 to 3 or any one of claims 4 to 6.

9. An electronic device, characterized in that, include: Memory, which stores computer programs; A processor reads a computer program stored in memory to perform the method described in any one of claims 1 to 3 or any one of claims 4 to 6.

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